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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Fratini, Antonio; Sansone, Mario; Bifulco, Paolo; Cesarelli, Mario (2015)
Publisher: BioMed Central
Journal: BioMedical Engineering OnLine
Languages: English
Types: Article
Subjects: Review, Biometric Identification; Electrocardiography; Humans; Signal Processing, Computer-Assisted; Statistics as Topic; Radiological and Ultrasound Technology; Biomaterials; Biomedical Engineering; Radiology, Nuclear Medicine and Imaging
Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Biel L, Pettersson O, Philipson L, Wide P. ECG analysis: a new approach in human identification. IEEE Trans Instrum Meas. 2001;50(3):808-12 (IEEE).
    • 2. Israel SA, Scruggs WT, Worek WJ, Irvine JM. Fusing face and ECG for personal identification. In: Proceedings of the 32nd applied imagery pattern recognition workshop, 2003. IEEE. 2003. p. 226-31.
    • 3. Kim KH, Bang SW, Kim SR. Emotion recognition system using short-term monitoring of physiological signals. Med Bio Eng Comput. 2004;42(3):419-27 (Springer).
    • 4. Kyoso M, Uchiyama A (2001) Development of an ECG identification system. In: IEMBS-01, vol 4, IEEE. 2001. p. 3721-3.
    • 5. Shen T-W, Tompkins WJ, Hu YH. Implementation of a one-lead ECG human identification system on a normal population. J Eng Comput Innov. 2011;2(1):12-21.
    • 6. Ballard L, Lopresti D, Monrose F. Forgery quality and its implications for behavioral biometric security. IEEE Trans Syst Man Cybern Part B Cybern. 2007;37(5):1107-18 (IEEE).
    • 7. Määttä J, Hadid A, Pietikäinen M. Face spoofing detection from single images using texture and local shape analysis. Biom IET. 2012;1(1):3-10.
    • 8. Palaniappan R, Krishnan SM. Identifying individuals using ECG beats. In: International conference on signal processing and communications, 2004 SPCOM '04, 2004. 2004. p. 569-72.
    • 9. Singh YN, Singh SK. Vitality detection from biometrics: state-of-the-art. In: World congress on information and communication technologies (WICT), 2011. 2011. p. 106-11.
    • 10. Van der Putte T, Keuning J. Biometrical fingerprint recognition: don't get your fingers burned. USA: Springer; 2000. p. 289-303.
    • 11. Biggio B, Akhtar Z, Fumera G, Marcialis GL, Roli F. Security evaluation of biometric authentication systems under real spoofing attacks. Iet Biom. 2012;1(1):11-24 (IET Digital Library).
    • 12. Galbally J, Alonso-Fernandez F, Fierrez J, Ortega-Garcia J. A high performance fingerprint liveness detection method based on quality related features. Future Gener Comput Syst. 2012;28(1):311-21.
    • 13. Hoekema R, Uijen GJ, van Oosterom A. Geometrical aspects of the interindividual variability of multilead ECG recordings. IEEE Trans Biomed Eng. 2001;48(5):551-9.
    • 14. Israel SA, Irvine JM, Cheng A, Wiederhold MD, Wiederhold BK. ECG to identify individuals. Pattern Recognit. 2005;38(1):133-42.
    • 15. Carreiras C, Lourenço A, Fred A, Ferreira R. ECG signals for biometric applications: are we there yet? In: ICINCO 2014-Proceedings of the 11th international conference on informatics in control, automation and robotics. 2014. p. 765-72.
    • 16. Kyoso M. A technique for avoiding false acceptance in ECG identification. In: IEEE EMBS asian-pacific conference on biomedical engineering, 2003. 2003. p. 190-1.
    • 17. Odinaka I, Lai P-H, Kaplan AD, O'Sullivan JA, Sirevaag EJ, Rohrbaugh JW. ECG biometric recognition: a comparative analysis. IEEE Trans Inf Forensics Secur. 2012;7(6):1812-24.
    • 18. Nasri B, Guennoun M, El-Khatib K. Using ECG as a measure in biometric identification systems. In: Science and technology for humanity (TIC-STH), 2009 IEEE Toronto international conference. 2009. p. 28-33.
    • 19. Israel SA, Irvine JM. Heartbeat biometrics: a sensing system perspective. IJCB. 2012;1(1):39.
    • 20. Abo-Zahhad M, Ahmed SM, Abbas SN. Biometric authentication based on PCG and ECG signals: present status and future directions. Signal Image Video Process. 2014;8(4):739-51.
    • 21. Bugdol MD, Mitas AW. Multimodal biometric system combining ECG and sound signals. Pattern Recognit Lett. 2014;38(1):107-12.
    • 22. Odinaka I, O'Sullivan JA, Sirevaag EJ, Rohrbaugh JW. Cardiovascular biometrics: combining mechanical and electrical signals. IEEE Trans Inf Forensics Secur. 2015;10(1):16-27.
    • 23. Mishra P, Singla SK. Artifact removal from biosignal using fixed point ICA algorithm for pre-processing in biometric recognition. Meas Sci Rev. 2013;13(1):7-11.
    • 24. Sidek KA, Khalil I. Enhancement of low sampling frequency recordings for ECG biometric matching using interpolation. Computer Methods Programs Biomed. 2013;109(1):13-25 (Elsevier).
    • 25. Kohler BU, Hennig C, Orglmeister R. The principles of software QRS detection. Eng Med Biol Mag IEEE. 2002;21(1):42-57.
    • 26. Shen T, Tompkins W. Biometric statistical study of one-lead ECG features and body mass index (BMI). Conf Proc IEEE Eng Med Biol Soc. 2005;2:1162-5.
    • 27. Zhang Z, Wei D. A new ECG identification method using Bayes' teorem. In: TENCON 2006, 2006 IEEE region 10 conference. 2006. p. 1-4.
    • 28. Fang S-C, Chan H-L. Human identification by quantifying similarity and dissimilarity in electrocardiogram phase space. Pattern Recognit. 2009;42(9):1824-31.
    • 29. Hurst JW. Naming of the waves in the ECG, with a brief account of their genesis. Circulation. 1998;98(18):1937-42.
    • 30. Singh YN, Gupta P. Biometrics method for human identification using electrocardiogram. In: Advances in biomet - rics. New York: Springer; 2009. p. 1270-9.
    • 31. Wübbeler G, Stavridis M, Kreiseler D, Bousseljot R-D, Elster C. Verification of humans using the electrocardiogram. Pattern Recognit Lett. 2007;28(10):1172-5 (Elsevier).
    • 32. Burch GE. The history of vectorcardiography. Med Hist Suppl. 1985;5:103-31.
    • 33. Chen C-K, Lin C-L, Chiu Y-M. Individual identification based on chaotic electrocardiogram signals. In: 2011 6th IEEE conference on industrial electronics and applications (ICIEA). IEEE. 2011. p. 1771-6.
    • 34. Fang S-C, Chan H-L. QRS detection-free electrocardiogram biometrics in the reconstructed phase space. Pattern Recognit Lett. 2013;34(5):595-602.
    • 35. Khalil I, Sufi F. Legendre polynomials based biometric authentication using QRS complex of ECG. In: International conference on intelligent sensors, sensor networks and information processing, 2008 ISSNIP 2008. IEEE. 2008. p. 297-302.
    • 36. Sufi F, Khalil I. An automated patient authentication system for remote telecardiology. In: International conference on intelligent sensors, sensor networks and information processing, 2008 ISSNIP 2008. IEEE. 2008. p. 279-84.
    • 37. Gargiulo GD, McEwan AL, Bifulco P, Cesarelli M, Jin C, Tapson J. Towards true unipolar ECG recording without the Wilson central terminal (preliminary results). Physiol Meas. 2013;34(9):991-1012.
    • 38. Morris F, Brady WJ, Camm J. ABC of clinical electrocardiography. New York: Wiley; 2009. p. 112.
    • 39. Gargiulo GD, McEwan AL, Bifulco P, Cesarelli M, Jin C, Tapson J, et al. Towards true unipolar bio-potential recording: a preliminary result for ECG. Physiol Meas. 2013;34(1):N1-7.
    • 40. Forsen GE, Nelson MR, Staron RJJ. Personal attributes authentication techniques. 1977 (In).
    • 41. Biel L, Pettersson O, Philipson L, Wide P. ECG analysis: a new approach in human identification. In: Proceedings of the 16th IEEE instrumentation and measurement technology conference, 1999 IMTC/99, vol 1. 1999. p. 557-61.
    • 42. Hoekema R, Uijen GJH, van Oosterom A. Geometrical aspects of the inter-individual variability of multilead ECG recordings. Comput Cardiol. 1999. p. 499-502.
    • 43. van Oosterom A, Hoekema R, Uijen GJH. Geometrical factors aefcting the interindividual variability of the ECG and the VCG. J Electrocardiol. 2000;33:219-27.
    • 44. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng BME. 1985;32(3):230-6.
    • 45. Kim K-S, Yoon T-H, Lee J-W, Kim D-J, Koo H-S. A robust human identification by normalized time-domain features of electrocardiogram. In: 27th annual international conference of the engineering in medicine and biology society, 2005 IEEE-EMBS 2005. 2005. p. 1114-7.
    • 46. Almeida R, Nez JPM, Rocha AP, Laguna P. Multilead ECG delineation using spatially projected leads from wavelet transform loops. IEEE Trans Biomed Eng. 2009;56(8):1996-2005.
    • 47. Yao J, Wan Y. A wavelet method for biometric identification using wearable ECG sensors. In 5th international summer school and symposium on medical devices and biosensors, 2008 ISSS-MDBS 2008. 2008. p. 297-300.
    • 48. Chan ADC, Hamdy MM, Badre A, Badee V. Wavelet distance measure for person identification using electrocardio - grams. IEEE Trans Instrum Meas. 2008;57(2):248-53.
    • 49. Chan ADC, Hamdy MM, Badre A, Badee V. Person identification using electrocardiograms. In: Canadian conference on electrical and computer engineering, 2006 CCECE '06. 2006. p. 1-4.
    • 50. Chiu C-C, Chuang C-M, Hsu C-Y. A novel personal identity verification approach using a discrete wavelet transform of the ECG signal. In: International conference on multimedia and ubiquitous engineering, 2008 MUE. IEEE. 2008. p. 201-6.
    • 51. Coutinho DP, Fred ALN, Figueiredo MAT. One-lead ECG-based personal identification using Ziv-Merhav cross parsing. In: 20th international conference on pattern recognition (ICPR), 2010. IEEE. 2010. p. 3858-61.
    • 52. Fratini A, Sansone M, Bifulco P, Romano M, Pepino A, Cesarelli M, et al. Individual identification using electrocar - diogram morphology. In: IEEE international symposium on medical measurements and applications proceedings (MeMeA), 2013. 2013. p. 107-10.
    • 53. Lourenco A, Silva H, Fred A. ECG-based biometrics: a real time classification approach. In: IEEE international work - shop on machine learning for signal processing (MLSP), 2012. 2012. p. 1-6.
    • 54. Odinaka I, Lai P-H, Kaplan AD, O'Sullivan JA, Sirevaag EJ, Kristjansson SD, et al. ECG biometrics: a robust short-time frequency analysis. In: IEEE international workshop on information forensics and security (WIFS), 2010. IEEE. 2010. p. 1-6.
    • 55. Poree F, Gallix A, Carrault G (2011) Biometric identification of individuals based on the ECG. Which conditions? Comput Cardiol 1999. p. 761-4 (IEEE).
    • 56. Shen J, Bao S-D, Yang L-C, Li Y. The PLR-DTW method for ECG based biometric identification. In: 2011 33rd annual international conference of the IEEE engineering in medicine and biology society. IEEE. 2011. p. 5248-51.
    • 57. Sidek KA, Khalil I. Person identification in irregular cardiac conditions using electrocardiogram signals. In: Engineer - ing in medicine and biology society, EMBC, 2011 annual international conference of the IEEE. 2011. p. 3772-5.
    • 58. Tashiro F, Aoyama T, Shimuta T, Ishikawa H, Shimatani Y, Ishijima M, et al. Individual identification with high frequency ECG: preprocessing and classification by neural network. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:2749-51.
    • 59. Wan Y, Yao J. A neural network to identify human subjects with electrocardiogram signals. In: Proceedings of the world congress on engineering and computer science 2008, WCECS 2008. 2008.
    • 60. Ye C, Coimbra MT, Kumar BVKV. Investigation of human identification using two-lead electrocardiogram (ECG) signals. In: Fourth IEEE international conference on biometrics: theory applications and systems (BTAS), 2010. IEEE. 2010. p. 1-8.
    • 61. Zhao Z, Yang L. ECG identification based on matching pursuit. In: 4th international conference on biomedical engineering and informatics (BMEI), 2011, vol 2. 2011. p. 721-4.
    • 62. Zheng G, Li Z-Y, Liu T-T, Dai M, editors. Study of human identification by electrocardiogram waveform morph. Berlin, Heidelberg: Springer; 2011.
    • 63. Tantawi M, Salem A, Tolba MF. Fiducial based approach to ECG biometrics using limited fiducial points. Commun Comput Inf Sci. 2014. p. 199-210.
    • 64. Fatemian SZ, Hatzinakos D. A new ECG feature extractor for biometric recognition. In: 16th international conference on digital signal processing, 2009. 2009. p. 1-6.
    • 65. Lourenço A, Silva H, Fred A. Unveiling the biometric potential of finger-based ECG signals. Comput Intell Neurosci. 2011;1:1-8 (Hindawi Publishing Corp.).
    • 66. Tawfik MM, Selim H, Kamal T. Human identification using time normalized QT signal and the QRS complex of the ECG. In: 7th international symposium on communication systems networks and digital signal processing (CSNDSP), 2010. 2010. p. 755-9.
    • 67. Lourenco A, Silva H, Santos DP, Fred A. Towards a finger based ECG biometric system. Biosignals. 2011;2011:348-53.
    • 68. Li M, Rozgić V, Thatte G, Lee S, Emken BA, Annavaram M, et al. Multimodal physical activity recognition by fusing temporal and cepstral information. IEEE Trans Neural Syst Rehabil Eng. 2010;18(4):369-80.
    • 69. Tsao Y-T, Shen T-W, Ko T-F, Lin T-H. The morphology of the eectrocardiogram for evaluating ECG biometrics. In: 9th international conference on e-health networking, application and services, 2007. 2007. p. 233-5.
    • 70. Safie SI, Soraghan JJ, Petropoulakis L. Electrocardiogram (ECG) biometric authentication using pulse active ratio (PAR). IEEE Trans Inf Forensics Secur. 2011;6(4):1315-22.
    • 71. Saechia S, Koseeyaporn J, Wardkein P. Human identification system based ECG signal. In: TENCON 2005, 2005 IEEE Region, vol 10. 2005. p. 1-4.
    • 72. Hou LS, Subari KS, Syahril S. QRS-complex of ECG-based biometrics in a two-level classifier. In: TENCON 2011, IEEE region 10 conference. 2011. p. 1159-63.
    • 73. Plataniotis KN, Hatzinakos D, Lee JKM. ECG biometric recognition without fiducial detection. In: Biometrics sympo - sium: special session on research at the biometric consortium conference, 2006. IEEE. 2006. p. 1-6.
    • 74. Fattah SA, Jameel ASMM, Goswami R, Saha SK, Syed N, Akter S, et al. An approach for human identification based on time and frequency domain features extracted from ECG signals. In: TENCON 2011, 2011 IEEE region 10 conference. IEEE. 2011. p. 259-63.
    • 75. Singh YN, Gupta P. ECG to individual identification. In: 2008 IEEE second international conference on biometrics: theory, applications and systems. IEEE. 2008. p. 1-8.
    • 76. Singh YN, Gupta P. Correlation-based classification of heartbeats for individual identification. Soft Comput. 2011;15(3):449-60 (Springer).
    • 77. Singh YN. Evaluation of electrocardiogram for biometric authentication. JIS. 2012;03(01):39-48.
    • 78. Tawfik MM, Kamal HST. Human identification using QT signal and QRS complex of the ECG. Online J Electron Electr Eng (OJEEE). 2011;3:1-5.
    • 79. Sansone M, Fratini A, Cesarelli M, Bifulco P, Pepino A, Romano M, et al. Influence of QT correction on temporal and amplitude features for human identification via ECG. In: 2013 IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS). 2013. p. 22-7.
    • 80. Sagie A, Larson MG, Goldberg RJ, Bengtson JR, Levy D. An improved method for adjusting the QT interval for heart rate (the Framingham Heart Study). Am J Cardiol. 1992;70(7):797-801.
    • 81. Sörnmo L, Laguna P. Bioelectrical signal processing in cardiac and neurological applications. Amsterdam: Elsevier; 2005.
    • 82. Fratini A, Bifulco P, Romano M, Clemente F, Cesarelli M. Simulation of surface EMG for the analysis of muscle activity during whole body vibratory stimulation. Comput Methods Programs Biomed. 2013;113(1):314-22.
    • 83. Agrafioti F, Hatzinakos D. ECG biometric analysis in cardiac irregularity conditions. SIViP. 2008;3(4):329-43.
    • 84. Agrafioti F, Hatzinakos D. ECG based recognition using second order statistics. In: 2008 6th annual communication networks and services research conference (CNSR). IEEE. 2008. p. 82-7
    • 85. Agrafioti F, Hatzinakos D. Fusion of ECG sources for human identification. In: 3rd international symposium on communications, control and signal processing, 2008 ISCCSP. 2008. p. 1542-7.
    • 86. Agrafioti F, Hatzinakos D. Signal validation for cardiac biometrics. In: 2010 IEEE international conference on acoustics speech and signal processing (ICASSP). IEEE. 2010. p. 1734-7.
    • 87. Wang Y, Agrafioti F, Hatzinakos D, Plataniotis KN. Analysis of human electrocardiogram for biometric recognition. EURASIP J Adv Signal Process. 2008;1:148658.
    • 88. Wang Y, Plataniotis KN, Hatzinakos D. Integrating analytic and appearance attributes for human identification from ECG signals. In: Special session on research at the biometric consortium conference, 2006 biometrics symposium. 2006. p. 1-6.
    • 89. Ziv J, Merhav N. A measure of relative entropy between individual sequences with application to universal classification. IEEE Trans Inf Theory. 1993;39(4):1270-9.
    • 90. Chen C-K, Lin C-L, Lin S-L, Chiu Y-M, Chiang C-T. A chaotic theorectical approach to ECG-based identity recognition [application notes]. IEEE Comput Intell Mag. 2014;9(1):53-63.
    • 91. Aghakabi A, Zokaee S. Fusing dorsal hand vein and ECG for personal identification. In: 2011 international confer - ence on electrical and control engineering (ICECE). 2011. p. 5933-6.
    • 92. Kouchaki S, Dehghani A, Omranian S, Boostani R. ECG-based personal identification using empirical mode decomposition and Hilbert transform. In: 2012 16th CSI international symposium on artificial intelligence and signal processing (AISP). 2012. p. 569-73.
    • 93. Loong JLC, Subari KS, Besar R, Abdullah MK. A new approach to ECG biometric systems: a comparative study between LPC and WPD systems. World Acad Sci Eng Technol. 2010;68:759-64.
    • 94. Zhao Z, Yang L, Chen D, Luo Y. A human ECG Identification system based on ensemble empirical mode decompo - sition. Sensors. 2013;13(5):6832-64.
    • 95. Zokaee S, Faez K. Human identification based on ECG and palmprint. Int J Electr Comput Eng (IJECE). 2012;2(2):261-6.
    • 96. Morettin PA. The Levinson algorithm and its applications in time series analysis. In: International statistical review/ revue internationale de statistique, vol 52. International Statistical Institute (ISI). 1984. p. 83-92 (vol 1).
    • 97. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A Math Phys Eng Sci. 1998;454(1971):903-95.
    • 98. IMAI S. Cepstral analysis synthesis on the mel frequency scale. In: Acoustics, speech, and signal processing, IEEE international conference on ICASSP '83, vol 8. IEEE Press Request Permissions. 1983. p. 93-6 (vol 3).
    • 99. Stevens SS, Volkmann J, Newman EB. A scale for the measurement of the psychological magnitude of pitch. J Acoust Soc Am. 1937;8:185-90.
    • 100. Ergin S, Uysal AK, Gunal ES, Gunal S, Gulmezoglu MB. ECG based biometric authentication using ensemble of features. In: Iberian conference on information systems and technologies, CISTI. 2014.
    • 101. Raj PS, Hatzinakos D. Feasibility of single-arm single-lead ECG biometrics. In: European signal processing conference. 2014. p. 2525-9.
    • 102. Goldberger AL, Amaral LAN, Glass L, Hausdorf JM, Ivanov PC, Mark RG. PhysioBank, PhysioToolkit, and PhysioNet : components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215-20.
    • 103. Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. Eng Med Biol Mag IEEE. 2001;20(3):45-50.
    • 104. Greenwald SD, Patil RS, Mark RG. Improved arrhythmia detection in noisy ECGs through the use of expert systems. In: Proceedings of the computers in cardiology, 1988. 1988. p. 163-5.
    • 105. Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput Cardiol. 1997;1997:673-6.
    • 106. Jager F, Taddei A, Moody GB, Emdin M, Antolič G, Dorn R. Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia. Med Bio Eng Comput. 2003;41(2):172-82 (Springer).
    • 107. Taddei A, Distante G, Emdin M, Pisani P, Moody GB, Zeelenberg C, et al. The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. Eur Heart J. 1992;13(9):1164-72.
    • 108. Moody GB, Goldberger A, McClennen S, Swiryn S. Predicting the onset of paroxysmal atrial fibrillation: the computers in cardiology challenge 2001. Comput Cardiol. 2001;2001:113-6.
    • 109. Bousseljot R, Kreiseler D, Schnabel A. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. In: Biomedizinische Technik/Biomedical Engineering. 1995. p. 317-8.
    • 110. Boumbarov O, Velchev Y, Sokolov S. Personal biometric identification based on ECG features. J Inf Technol Control. 2008;3(4):11-8.
    • 111. Irvine JM, Israel SA. A sequential procedure for individual identity verification using ECG. In: EURASIP J Adv Signal Process, 6th edn. 2009. p. 42-57 (vol 5).
    • 112. Coutinho D, Silva H, Gamboa H, Fred A, Figueiredo M. Novel fiducial and non-fiducial approaches to electrocardio - gram-based biometric systems. Biom IET. 2013;2(2):64-75.
    • 113. Silva H, Lourenco A, Lourenco R, Leite P, Coutinho D, Fred A. Study and evaluation of a single diefrential sensor design based on electro-textile electrodes for ECG biometrics applications. In: Sensors, 2011 IEEE. 2011. p. 1764-7.
    • 114. Sriram JC, Shin M, Choudhury T, Kotz D. Activity-aware ECG-based patient authentication for remote health monitoring. In: Proceedings of the 2009 international conference on multimodal interfaces. ACM; 2009. p. 297-304.
    • 115. Derawi M. Wireless chest-based ECG biometrics. In: Lecture notes in electrical engineering, vol 330. 2015. p. 566-79.
    • 116. Derawi M, Voitenko I, Endrerud PE. Real-time wireless ECG biometrics with mobile devices. In: Proceedings-2014 international conference on medical biometrics, ICMB 2014. 2014. p. 151-6.
    • 117. Zheng G, Yu T. Study of hybrid strategy for ambulatory ECG waveform clustering. J Softw. 2011;6(7):1257-64.
    • 118. Chen Y, Li Y, Cheng X-Q, Guo L. Survey and taxonomy of feature selection algorithms in intrusion detection system. In: Information security and cryptology, vol 4318. Berlin, Heidelberg: Springer; 2006. p. 153-67 (Chapter 13).
    • 119. Morrison DF. Multivariate statistical methods. In: McGraw Hill professional. 1976.
    • 120. Boumbarov O, Velchev Y, Sokolov S. ECG personal identification in subspaces using radial basis neural networks. In: 2009 IEEE international workshop on intelligent data acquisition and advanced computing systems: technology and applications, 2009 IDAACS. 2009. p. 446-51.
    • 121. Zhao C, Wysocki T, Agrafioti F, Hatzinakos D. Securing handheld devices and fingerprint readers with ECG biometrics. In: 2012 IEEE fifth international conference on biometrics: theory, applications and systems (BTAS). 2012. p. 150-5.
    • 122. Gahi Y, Lamrani M, Zoglat A, Guennoun M, Kapralos B, El-Khatib K. Biometric identification system based on electrocardiogram data. In: New technologies, mobility and security, 2008 NTMS '08. 2008. p. 1-5.
    • 123. Guennoun M, Guennoun ZE, El-Khatib K. Eficiency in the design of a biometric identification system based on electrocardiogram data. In: International conference on computer engineering and applications. 2009. p. 1-5.
    • 124. Tantawi MM, Revett K, Salem A, Tolba MF. Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition. J Intell Inf Syst. 2012;40(1):17-39.
    • 125. Mitchell TM. Machine learning. In: McGraw-Hill, Inc. 1997. p. 432.
    • 126. Sasikala P, Wahidauanu R. Identification of individuals using electrocardiogram. In: International journal of computer science and network security. 2010.
    • 127. Silva H, Gamboa H, Fred A. One lead ECG based personal identification with feature subspace ensembles. In: Lecture notes in computer science, vol 4571. Berlin, Heidelberg: Springer; 2007. p. 770-83.
    • 128. Silva H, Gamboa H, Fred A. Applicability of lead v2 ECG measurements in biometrics. In: Med-e-Tel proceedings. 2007.
    • 129. Venkatesh N, Jayaraman S. Human electrocardiogram for biometrics using DTW and FLDA. In: 2010 20th international conference on pattern recognition (ICPR). 2010. p. 3838-41.
    • 130. Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006. p. 738.
    • 131. Mai V, Khalil I, Meli C. ECG biometric using multilayer perceptron and radial basis function neural networks. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:2745-8.
    • 132. Pathoumvanh S, Airphaiboon S, Hamamoto K. Robustness study of ECG biometric identification in heart rate variability conditions. IEEJ Trans Electr Electron Eng. 2014;9(3):294-301.
    • 133. Venugopalan S, Savvides M, Griofa MO, Cohen K. Analysis of low-dimensional radio-frequency impedance-based cardio-synchronous waveforms for biometric authentication. IEEE Trans Biomed Eng. 2014;61(8):2324-35.
    • 134. Lin SL, Chen CK, Lin CL, Yang WC, Chiang CT. Individual identification based on chaotic electrocardiogram signals during muscular exercise. IET Biom. 2014;3(4):257-66.
    • 135. Porée F, Kervio G, Carrault G. ECG biometric analysis in diefrent physiological recording conditions. Signal, image and video processing. 2014.
    • 136. Sidek KA, Mai V, Khalil I. Data mining in mobile ECG based biometric identification. J Netw Comput Appl. 2014;44:83-91.
    • 137. Wahabi S, Pouryayevali S, Hari S, Hatzinakos D. On evaluating ECG biometric systems: session-dependence and body posture. IEEE Trans Inf Forensics Secur. 2014;9(11):2002-13.
    • 138. Wang Z, Zhang Y. Research on ECG biometric in cardiac irregularity conditions. In: Proceedings-2014 international conference on medical biometrics, ICMB 2014. 2014. p. 157-63.
    • 139. Sidek KA, Khalil I. Automobile driver recognition under diefrent physiological conditions using the electrocardio - gram. Comput Cardiol. 2011. p. 753-6.
    • 140. Sidek KA, Khalil I, Jelinek HF. ECG biometric with abnormal cardiac conditions in remote monitoring system. IEEE Trans Syst Man Cybern Syst. 2014;44(11):1498-509.
    • 141. Tan WC, Yeap HM, Chee KJ, Ramli DA. Towards real time implementation of sparse representation classifier (SRC) based heartbeat biometric system. In: Lecture notes in electrical engineering, vol 307. 2014. p. 189-202.
    • 142. Lee S, Park SY, Kim SJ, Joeng JH, Kim SM. A study on a bio-signal biometric algorithm on the ubiquitous environments. In: Lecture notes in electrical engineering, vol 280. 2014. p. 691-7.
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